Pathways between under/unemployment and health among racialized immigrant women in Toronto
Bibliographic record
Abstract
OBJECTIVE: We sought to document pathways between under/unemployment and health among racialized immigrant women in Toronto while exploring the ways in which gender, class, migration and racialization, as interlocking systems of social relations, structure these relationships. DESIGN: We conducted 30 interviews with racialized immigrant women who were struggling to get stable employment that matched their education and/or experience. Participants were recruited through flyers, partner agencies and peer researcher networks. Most interviews (21) were conducted in a language other than English. Interviews were transcribed, translated as appropriate and analyzed using NVivo software. The project followed a community-based participatory action research model. RESULTS: Under/unemployment negatively impacted the physical and mental health of participants and their families. It did so directly, for example through social isolation, as well as indirectly through representation in poor quality jobs. Under/unemployment additionally led to the intensification of job search strategies and of the household/caregiving workload which also negatively impacted health. Health problems, in turn, contributed to pushing participants into long-term substandard employment trajectories. Participants' experiences were heavily structured by their social location as low income racialized immigrant women. CONCLUSIONS: Our study provides needed qualitative evidence on the gendered and racialized dimensions of under/unemployment, and adverse health impacts resulting from this. Drawing on intersectional analysis, we unpack the role that social location plays in creating highly uneven patterns of under/unemployment and negative health pathways for racialized immigrant women. We discuss equity informed strategies to help racialized immigrant women overcome barriers to stable work that match their education and/or experience.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".